{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RRHD3BKIBLTOFOZN6BJRONE7RC","short_pith_number":"pith:RRHD3BKI","schema_version":"1.0","canonical_sha256":"8c4e3d85480ae6e2bb2df05317349f88b878fbe990f4949cd27862004bdc9f7b","source":{"kind":"arxiv","id":"2603.04724","version":2},"attestation_state":"computed","paper":{"title":"An explicit finite-memory scheme for approximating and sampling invariant measures of stochastic functional differential equations with infinite delay","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Guozhen Li, Shan Huang, Xiaoyue Li, Xuerong Mao","submitted_at":"2026-03-05T01:54:26Z","abstract_excerpt":"Efficient sampling and numerical approximation of invariant probability measures (IPMs) on infinite-dimensional function spaces are important problems in scientific computing. In this paper, we study the numerical approximation and sampling of IPMs associated with stochastic functional differential equations with infinite delay (SFDEswID). To this end, we develop a fully explicit ergodicity-preserving truncated Euler--Maruyama scheme for SFDEswID that requires only finite historical storage and accommodates superlinearly growing coefficients. We establish strong convergence of the numerical se"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2603.04724","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2026-03-05T01:54:26Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"ce4f7b018eec9ada896104e003b0f8cd0d89ec71e29c6762205dea3479fbc1b6","abstract_canon_sha256":"5e15818d2ac6e4a087c54ef3b43da2194a4f61b517acf5ad864326382eee42a8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:07:23.132902Z","signature_b64":"d8pXCO7Rk2ixRaWAeXfqEOLqmaM4DEM2oS49YIOAbCBXrSAoHQBVhtuFNhj1n275zDQ0/zMENxSp73S5J27sBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c4e3d85480ae6e2bb2df05317349f88b878fbe990f4949cd27862004bdc9f7b","last_reissued_at":"2026-06-09T02:07:23.131896Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:07:23.131896Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An explicit finite-memory scheme for approximating and sampling invariant measures of stochastic functional differential equations with infinite delay","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Guozhen Li, Shan Huang, Xiaoyue Li, Xuerong Mao","submitted_at":"2026-03-05T01:54:26Z","abstract_excerpt":"Efficient sampling and numerical approximation of invariant probability measures (IPMs) on infinite-dimensional function spaces are important problems in scientific computing. In this paper, we study the numerical approximation and sampling of IPMs associated with stochastic functional differential equations with infinite delay (SFDEswID). To this end, we develop a fully explicit ergodicity-preserving truncated Euler--Maruyama scheme for SFDEswID that requires only finite historical storage and accommodates superlinearly growing coefficients. We establish strong convergence of the numerical se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.04724","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2603.04724/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2603.04724","created_at":"2026-06-09T02:07:23.132049+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.04724v2","created_at":"2026-06-09T02:07:23.132049+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.04724","created_at":"2026-06-09T02:07:23.132049+00:00"},{"alias_kind":"pith_short_12","alias_value":"RRHD3BKIBLTO","created_at":"2026-06-09T02:07:23.132049+00:00"},{"alias_kind":"pith_short_16","alias_value":"RRHD3BKIBLTOFOZN","created_at":"2026-06-09T02:07:23.132049+00:00"},{"alias_kind":"pith_short_8","alias_value":"RRHD3BKI","created_at":"2026-06-09T02:07:23.132049+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RRHD3BKIBLTOFOZN6BJRONE7RC","json":"https://pith.science/pith/RRHD3BKIBLTOFOZN6BJRONE7RC.json","graph_json":"https://pith.science/api/pith-number/RRHD3BKIBLTOFOZN6BJRONE7RC/graph.json","events_json":"https://pith.science/api/pith-number/RRHD3BKIBLTOFOZN6BJRONE7RC/events.json","paper":"https://pith.science/paper/RRHD3BKI"},"agent_actions":{"view_html":"https://pith.science/pith/RRHD3BKIBLTOFOZN6BJRONE7RC","download_json":"https://pith.science/pith/RRHD3BKIBLTOFOZN6BJRONE7RC.json","view_paper":"https://pith.science/paper/RRHD3BKI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.04724&json=true","fetch_graph":"https://pith.science/api/pith-number/RRHD3BKIBLTOFOZN6BJRONE7RC/graph.json","fetch_events":"https://pith.science/api/pith-number/RRHD3BKIBLTOFOZN6BJRONE7RC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RRHD3BKIBLTOFOZN6BJRONE7RC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RRHD3BKIBLTOFOZN6BJRONE7RC/action/storage_attestation","attest_author":"https://pith.science/pith/RRHD3BKIBLTOFOZN6BJRONE7RC/action/author_attestation","sign_citation":"https://pith.science/pith/RRHD3BKIBLTOFOZN6BJRONE7RC/action/citation_signature","submit_replication":"https://pith.science/pith/RRHD3BKIBLTOFOZN6BJRONE7RC/action/replication_record"}},"created_at":"2026-06-09T02:07:23.132049+00:00","updated_at":"2026-06-09T02:07:23.132049+00:00"}